nQuery Advisor
- Statistical software for designing and analyzing experiments with complex sample designs and multiple factors.
- Commonly used to optimize processes or products and to evaluate treatments in engineering, manufacturing, and life sciences.
- Supports analysis of effects and interactions, sample size and power calculations, and tests of statistical significance.
Definition
Section titled “Definition”nQuery Advisor is a statistical software package that is used to design and analyze experiments, particularly those involving complex sample designs and multiple factors.
Explanation
Section titled “Explanation”nQuery Advisor helps researchers and practitioners plan experiments and analyze experimental results. It is used to identify main effects and interactions among factors, determine optimal factor levels, calculate statistical power and sample size, and test for statistical significance. The software is especially useful in contexts where experimentation is applied to optimize processes or products or to evaluate treatments, and it is commonly applied in engineering, manufacturing, and the life sciences. It can also be used to adjust for confounding factors in analyses.
Examples
Section titled “Examples”Example 1: Optimizing a manufacturing process
Section titled “Example 1: Optimizing a manufacturing process”Suppose a company that manufactures electronic devices wants to optimize its production process to reduce defects and improve yield. They decide to conduct an experiment to identify the factors that have the greatest impact on the process, and to determine the optimal levels of these factors.
The company sets up a full factorial experiment with three factors: temperature, pressure, and time. Each factor has two levels: low and high. The company randomly assigns each combination of factor levels to a different run of the production process, and measures the percentage of defective products produced in each run.
Using nQuery Advisor, the company can analyze the data from the experiment to identify the main effects and interactions of the factors on the process, and determine the optimal levels of each factor that minimize defects and maximize yield. They can also use nQuery Advisor to calculate statistical power and sample size, and to test for statistical significance.
Example 2: Evaluating the effectiveness of a new drug
Section titled “Example 2: Evaluating the effectiveness of a new drug”Suppose a pharmaceutical company is developing a new drug to treat a particular disease. They want to determine the optimal dosage of the drug, and to evaluate its effectiveness in different patient populations.
The company conducts a clinical trial with a sample of patients who have the disease. They randomly assign the patients to one of four treatment groups: a low-dose group, a medium-dose group, a high-dose group, and a placebo group. The patients are followed for a period of time, and the company measures the improvement in their condition.
Using nQuery Advisor, the company can analyze the data from the trial to determine the optimal dosage of the drug and to compare the effectiveness of the different dosages. They can also use nQuery Advisor to adjust for confounding factors, such as age and gender, and to test for statistical significance.
Use cases
Section titled “Use cases”- Designing and analyzing experiments to optimize processes or products.
- Evaluating the effectiveness of treatments in clinical trials.
- Applications in engineering, manufacturing, and the life sciences.
Related terms
Section titled “Related terms”- Statistical power
- Sample size
- Full factorial experiment
- Main effects
- Interactions
- Confounding factors
- Clinical trial
- Placebo